Machine Learning: An Applied Mathematics Introduction
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Machine Learning: An Applied Mathematics Introduction
An accessible introduction for anyone who wants to understand the foundations and put the tools into practice.
Machine Learning: An Applied Mathematics Introduction
Artikelnr.: 15487278

Machine Learning: An Applied Mathematics Introduction

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Artikelnr.: 15487278

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An accessible introduction for anyone who wants to understand the foundations and put the tools into practice.
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Besondere Merkmale

Applied Approach
This book emphasizes practical applications of machine learning concepts, bridging the gap between theory and practice, making it ideal for students and professionals seeking real-world implementations.
Mathematical Foundation
It provides a strong mathematical foundation essential for understanding machine learning algorithms, ensuring readers develop both the theoretical and computational skills necessary for advanced studies or industry roles.
Comprehensive Coverage
The text covers a wide range of machine learning topics, from basic concepts to advanced techniques, making it a valuable resource for a diverse audience, including novices and seasoned practitioners.

Produktdetails

Discover the power of Machine Learning with this applied mathematics introduction. Order online from Ubuy Austria and explore the world of artificial intelligence.
  • Fully self-contained introduction to machine learning
  • Requires understanding of basics of matrix algebra and calculus
  • Covers essential mathematics behind important techniques
  • Includes real-world examples from various fields
  • Authored by Paul Wilmott, an experienced educator in the field
  • Accessible introduction for anyone wanting to understand foundations and apply tools
Publisher Panda Ohana Publishing
Publication date May 26, 2019
Language English
Print length 242 pages
ISBN-10 1916081606
ISBN-13 978-1916081604
Item Weight 12.2 ounces (345.87 grams)
Dimensions 6.14 x 0.55 x 9.21 inches (15.6 x 1.4 x 23.4 cm)

Für wen ist das Produkt geeignet?

Suitable For
  • Undergraduate Students

    Ideal for students seeking foundational knowledge in machine learning with a focus on applied mathematics principles.

  • Data Science Practitioners

    Beneficial for professionals looking to deepen their understanding of mathematical concepts in machine learning applications.

  • Self-learners

    Perfect for independent learners who wish to explore machine learning concepts at their own pace using mathematics.

Not Suitable For
  • Beginners

    Not suitable for complete beginners without prior knowledge of mathematics or programming concepts in machine learning.

PRODUKTBESCHREIBUNG

Machine Learning: An Applied Mathematics Introduction

About This Item

Are you ready to dive into the exciting world of Machine Learning? Look no further than our product title, "Machine Learning: An Applied Mathematics Introduction." This comprehensive guide combines the power of applied mathematics with the fascinating concept of machine learning to bring you a wealth of knowledge and practical application. Through the use of various Machine Learning algorithms, this book explores the ways in which applied mathematics can enhance your understanding and implementation of machine learning models. Gain a solid foundation in the principles of applied mathematics for machine learning and discover how these techniques can be used to solve real-world problems. Whether you're a student or a professional, this book is the perfect introduction to Machine Learning and applied mathematics. Learn the essential concepts and techniques needed to thrive in this rapidly growing field.

From understanding the foundational principles to exploring cutting-edge frameworks, each chapter will provide you with valuable insights and actionable information. Discover the wide range of applications for Machine Learning and Applied Mathematics across various industries. From business optimization to data analysis, finance to research, this book will show you how applied mathematics can revolutionize the way you approach these areas. Gain the knowledge and skills needed to leverage Machine Learning and applied mathematics to drive innovation and success in your chosen field. Don't miss out on the opportunity to master the intersection of Machine Learning and applied mathematics.

Empower yourself with the tools and techniques needed to excel in this dynamic field. Grab your copy of "Machine Learning: An Applied Mathematics Introduction" today and embark on a journey of endless possibilities.

Haben Sie eine Frage? Mit uns chatten

Kundenfragen und -antworten

  • Frage: Who is the author of this book?

    Antworten: Paul Wilmott
  • Frage: What are some of the fields that the book covers?

    Antworten: Finance, economics, politics, biology, sociology, gambling, and marketing
  • Frage: Is this book suitable for beginners?

    Antworten: Yes, it is accessible for anyone with an understanding of matrix algebra and calculus
  • Frage: Does this book require a strong math background?

    Antworten: Yes, the book assumes a background in applied mathematics and jumps between high-level and technical discussions.
  • Frage: Is this book suitable for machine learning beginners?

    Antworten: It may disappoint beginners without sufficient math knowledge, as the book assumes a certain level of math background.
  • Frage: Does the book provide a concise and rigorous introduction to machine learning?

    Antworten: No, it jumps between high-level discussion and technical detail, lacking clarity in explaining certain topics.
  • Frage: Is the book well-organized with useful examples and diagrams?

    Antworten: Yes, it is well-organized and provides lots of useful examples and diagrams.

Applied Editorial Review

Machine Learning: An Applied Mathematics Introduction is a thoughtfully crafted resource that caters to readers with varied backgrounds in mathematics. The book, published by Panda Ohana Publishing, spans 242 pages and is written in a straightforward style aimed at demystifying complex machine learning algorithms for those new to the subject. Reviewers appreciate its ease of understanding, particularly for individuals lacking a strong mathematical foundation, though some caution that deeper mathematical concepts may still pose challenges. This balance of intuitive explanation combined with essential detail makes it a valuable companion for both beginners and those seeking a refresher in machine learning concepts without delving into extensive coding. However, readers with advanced knowledge may find the discussions somewhat high-level or lacking in rigor.

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Vorteile

  • Accessible for readers with non-mathematical backgrounds
  • Clear explanations of machine learning concepts
  • Informal tone makes for enjoyable reading
  • Good overview of key machine learning ideas
  • Helpful for beginners to grasp foundational mathematics

Nachteile

  • May not delve deeply enough for seasoned practitioners

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Merkmale und Vorteile

  • Fully self-contained introduction to machine learning
  • Covers the essential mathematics behind the most important techniques
  • Includes real-world examples from various fields
  • Accessible for those with basic understanding of matrix algebra and calculus
  • Written by Paul Wilmott, a renowned expert in the field
  • Great resource for anyone looking to learn and apply machine learning

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